arXiv subjects

Vasilis Friderikos

Publications and source records attributed to Vasilis Friderikos.

At least 19 recordsLinked to original sources

arXiv2025

Anchor-and-Connect: Robotic Aerial Base Stations Transforming 6G Infrastructure

Despite the significant attention that aerial base stations (ABSs) have received recently, their practical implementation is severely weakened by their limited endurance due to the battery constraints of drones. To overcome this fundamental limitation and barrier for wider adoption, we propose the concept of robotic aerial base stations (RABSs) that are equipped with energy-neutral anchoring end-effectors able to autonomously grasp or perch on tall urban landforms. Thanks to the energy-efficient anchoring operation, RABSs could offer seamless wireless connectivity for multiple hours compared to minutes of the typical hovering-based ABSs. Therefore, the prolonged service capabilities of RABSs allowing them to integrate into the radio access network and augment the network capacity where and when needed. To set the scene, we discuss the key components of the proposed RABS concept including hardware, workflow, communication considerations, and regulation issues. Then, the advantages of RABSs are highlighted which is followed by case studies that compare RABSs with terrestrial micro BSs and other types of non-terrestrial communication infrastructure, such as hovering-based, tethered, and laser-powered ABSs.

cs.ET↗

arXiv2025

Joint Robotic Aerial Base Station Deployment and Wireless Backhauling in 6G Multi-hop Networks

Due to their ability to anchor into tall urban landforms, such as lampposts or street lights, robotic aerial base stations (RABSs) can create a hyper-flexible wireless multi-hop heterogeneous network to meet the forthcoming green, densified, and dynamic network deployment to support, inter alia, high data rates. In this work, we propose a network infrastructure that can concurrently support the wireless backhaul link capacity and access link traffic demand in the millimeter-wave (mmWave) frequency band. The RABSs grasping locations, resource blocks (RBs) assignment, and route flow control are simultaneously optimized to maximize the served traffic demands. Robotic base stations capitalize on the fact that traffic distribution varies considerably across both time and space within a given geographical area. Hence, they are able to relocate to suitable locations, i.e., 'follow' the traffic demand as it unfolds to increase the overall network efficiency. To tackle the curse of dimensionality of the proposed mixed-integer linear problem, we propose a greedy algorithm to obtain a competitive solution with low computational complexity. Compared to baseline models, which are heterogeneous networks with randomly deployed fixed small cells and pre-allocated RBs for wireless access and backhaul links, a wide set of numerical investigations reveals that robotic base stations could improve the served traffic demand. Specifically, the proposed mode serves at most 65\% more traffic demand compared to an equal number of deployed fixed small cells.

cs.NI↗

arXiv2023

Swarm of Robotic Aerial Base Stations for mmWave Multi-Hop Backhauling

Robotic aerial base stations (RABSs) that are able to anchor at tall urban landforms are expected to bring further flexibility to millimeter-wave (mmWave) multi-hop backhaul networks in highly dense urban environments. In this paper, a swarm of RABSs are deployed to construct a dynamic mmWave backhaul network according to the traffic spatial distribution, and relocate their positions in subsequent time epochs according to the traffic temporal dynamic. The overall energy efficiency of the proposed framework is maximized by determining the RABS deployment, relocation and route formation under the channel capacity and hop constraints. The problem is formulated as a mixed-integer linear fractional programming (MILFP) and a two-stage method is developed to overcome the computational complexity. A wide set of numerical investigations reveal that compared to fixed small cells, only half as many RABSs are required to cover the same volume of traffic demand.

eess.SY↗

arXiv2023

Aerial IRS with Robotic Anchoring Capabilities: A Novel Way for Adaptive Coverage Enhancement

It is widely accepted that integrating intelligent reflecting surfaces (IRSs) with unmanned aerial vehicles (UAV) or drones can assist wireless networks in improving network coverage and end user Quality of Service (QoS). However, the critical constrain of drones is their very limited hovering/flying time. In this paper we propose the concept of robotic aerial IRSs (RA-IRSs), which are in essence drones that in addition to IRS embed an anchoring mechanism that allows them to grasp in an energy neutral manner at tall urban landforms such as lampposts. By doing so, RA-IRSs can completely eliminate the flying/hovering energy consumption and can offer service for multiple hours or even days (something not possible with UAV-mounted IRSs). Using that property we show how RA-IRS can increase network performance by changing their anchoring location to follow the spatio-temporal traffic demand. The proposed methodology, developed through Integer Linear Programming (ILP) formulations offers a significant Signal-to-Noise (SNR) gain in highly heterogeneous regions in terms of traffic demand compared to fixed IRS; hence, addressing urban coverage discrepancies effectively. Numerical simulations validate the superiority of RA-IRSs over fixed terrestrial IRSs in terms of traffic serviceability, sustaining more than 2 times the traffic demand in areas experiencing high heterogeneity, emphasizing their adaptability in improving coverage and QoS in complex urban terrains.

cs.NI↗

arXiv2023

Robust Deployment and Resource Allocation for Robotic Aerial Base Station Enabled OFDM Integrated Sensing and Communication

The envisioned robotic aerial base station (RABS) concept is expected to bring further flexibility to integrated sensing and communication (ISAC) systems. In this letter, characterizing the spatial traffic distribution on a grid-based model, the RABS-assisted ISAC system is formulated as a robust optimization problem to maximize the minimum satisfaction rate (SR) under a cardinality constrained uncertainty set. The problem is reformulated as a mixed-integer linear programming (MILP) and solved approximately by the iterative linear programming rounding algorithm. Numerical investigations show that the minimum SR can be improved by 28.61% on average compared to fixed small cells.

cs.NI↗

arXiv2023

Optimal Mobility Aware Wireless Edge Cloud Support for the Metaverse

Mobile augmented reality (MAR) applications extended in the metaverse could provide mixed and immersive experiences by amalgamating the virtual and physical world. However, the joint consideration between MAR and metaverse seeks the reliable and high quality support for foreground interactions and background contents from these applications, which intensifies their consumption in energy, caching and computing resources. To tackle these challenges, a more flexible request assignment and resource allocation with more efficient processing are proposed in this paper through anchoring decomposed metaverse AR services at different edge nodes and proactively caching background metaverse region models embedded with target Augmented Reality Objects (AROs).Advanced terminals are also considered to further reduce service delay at an acceptable cost of energy consumption.We then propose and solve a joint optimization problem that explicitly considers the balance between service delay and energy consumption under the constraint of user perception quality in a mobility event.By also explicitly taking into account capabilities of user terminals, the proposed optimized scheme is compared to its terminal oblivious version in this paper. According to a wide set of numerical investigations, the proposed scheme owns advantages in service latency and energy efficiency over other nominal baseline schemes which neglect capacities of terminals, user physical mobility, service decomposition and the inherent multi modality of the metaverse MAR service.

cs.NI↗

arXiv2022

Mobility Aware Optimization in the Metaverse

Metaverse applications that incorporate Mobile Augmented Reality (MAR) provide mixed and immersive experiences by amalgamating the virtual with the physical world. Notably, due to their multi-modality such applications are demanding in terms of energy consumption, computing and caching resources to efficiently support foreground interactions of participating users and rich background content. In this paper, the metaverse service is decomposed and anchored at suitable edge caching/computing nodes in 5G and beyond networks to enable efficient processing of background metaverse region models embedded with target AROs. To achieve that, a joint optimization problem is proposed, which explicitly considers the user physical mobility, service decomposition, and the balance between service delay, user perception quality and power consumption. A wide set of numerical investigations reveal that, the proposed scheme could provide optimal decision making and outperform other nominal baseline schemes which are oblivious of user mobility as well as do not consider service decomposition.

cs.NI↗

arXiv2022

Multi-Objective Provisioning of Network Slices using Deep Reinforcement Learning

Network Slicing (NS) is crucial for efficiently enabling divergent network applications in next generation networks. Nonetheless, the complex Quality of Service (QoS) requirements and diverse heterogeneity in network services entails high computational time for Network Slice Provisioning (NSP) optimization. The legacy optimization methods are challenging to meet the low latency and high reliability of network applications. To this end, we model the real-time NSP as an Online Network Slice Provisioning (ONSP) problem. Specifically, we formulate the ONSP problem as an online Multi-Objective Integer Programming Optimization (MOIPO) problem. Then, we approximate the solution of the MOIPO problem by applying the Proximal Policy Optimization (PPO) method to the traffic demand prediction. Our simulation results show the effectiveness of the proposed method compared to the state-of-the-art MOIPO solvers with a lower SLA violation rate and network operation cost.

cs.NI↗

arXiv2022

Interference Aware Path Planning for Mobile Robots in mmWave Multi Cell Networks

The emerging beyond 5G and envisioned 6G wireless networks are considered as key enablers in supporting a diversified set of applications for industrial mobile robots (MRs). The scenario under investigation in this paper relates to mobile robots that autonomously roam in an industrial floor and perform a variety of tasks at different locations whilst utilizing high directivity beamformers in mmWave small cells. In such scenarios, the potential close proximity of mobile robots connected to different base stations, may cause excessive levels of interference having as a net result a decrease in the overall achievable data rate in the network. To resolve this issue, a novel mixed integer linear programming formulation is proposed where the trajectory of the mobile robots is considered jointly with the interference level at different beam sectors. Therefore, creating a low interference path for each mobile robot in the industrial floor. A wide set of numerical investigations reveal that the proposed path planning optimization approach for the mmWave connected mobile robots can improve the overall achievable throughput by up to 31% compared to an interference oblivious scheme, without penalizing the overall travelling time.

eess.SY↗

arXiv2022

Energy and Age Pareto Optimal Trajectories in UAV-assisted Wireless Data Collection

This paper studies an unmanned aerial vehicle (UAV)-assisted wireless network, where a UAV is dispatched to gather information from ground sensor nodes (SN) and transfer the collected data to the depot. The information freshness is captured by the age of information (AoI) metric, whilst the energy consumption of the UAV is seen as another performance criterion. Most importantly, the AoI and energy efficiency are inherently competing metrics, since decreasing the AoI requires the UAV returning to the depot more frequently, leading to a higher energy consumption. To this end, we design UAV paths that optimize these two competing metrics and reveal the Pareto frontier. To formulate this problem, a multi-objective mixed integer linear programming (MILP) is proposed with a flow-based constraint set and we apply Bender's decomposition on the proposed formulation. The overall outcome shows that the proposed method allows deriving non-dominated solutions for decision making for UAV based wireless data collection. Numerical results are provided to corroborate our study by presenting the Pareto front of the two objectives and the effect on the UAV trajectory.

cs.NI↗

arXiv2022

Max-min Rate Deployment Optimization for Backhaul-limited Robotic Aerial 6G Small Cells

To overcome the limited on-board battery issue of nominal airborne base stations (ABSs), we are exploring the use of robotic airborne base station (RABS) with energy neutral grasping end-effectors that are able to autonomously perch at tall urban landforms. Specifically, this paper studies a heterogeneous network (HetNet) assisted by a movable RABS as a small cell which connects to a macro base station (MBS) through a limited-capacity wireless backhaul link, which can be deemed as another major challenge. To exploit the potential gains that the mobility of RABS can bring in the system, the minimum rate among all users is maximized by jointly optimizing the RABS deployment, user association and subcarrier allocation. This problem is initially formulated as a binary polynomial optimization (BPO) problem. After reformulating it as a nonconvex quadratically constrained quadratic programming (QCQP), we propose a semidefinite relaxation (SDR) based heuristic method to capture a high-quality solution in polynomial time. Numerical results reveal that deploying a RABS as the small cell can improve the minimum data rate by 95.43% at most and 33.97% on average, and the developed SDR heuristic algorithm significantly outperforms the linear relaxation (LR) baseline method.

cs.NI↗

arXiv2022

Optimal Deployment and Operation of Robotic Aerial 6G Small Cells with Grasping End Effectors

Although airborne base stations (ABSs) mounted on drones show a significant potential to enhance network capacity and coverage due to their flexible deployment, the system performance is severely limited by the endurance of the on-board battery. To overcome this key shortcoming, we are exploring robotic airborne base station (RABS) with energy neutral grasping end-effectors able to autonomously perch at tall urban landforms. This paper studies the optimal deployment (fly to another grasping location or remain in the same one) and operation (active or sleep at an epoch) of RABS based on the spatio-temporal characteristics of underlying traffic demand from end-users. Specifically, an integer linear programming (ILP) is formulated by exploiting the coupling between these two decisions, that is, the RABS only needs to visit the locations where it is active. A Lagrangian heuristic algorithm is then proposed by exploiting the totally unimodular structure of the ILP formulation. A wide set of numerical investigations reveal that thanks to its mobility, a single robotic aerial small cell is able to outperform five (5) fixed small cells in terms of served user generated traffic within a 16 to 41 hours period.

cs.NI↗

arXiv2022

Robotic Aerial 6G Small Cells with Grasping End Effectors for mmWave Relay Backhauling

Deployment of small cells in dense urban areas dedicated to the heterogeneous network (HetNet) and associated relay nodes for improving backhauling is expected to be an important structural element in the design of beyond 5G (B5G) and 6G wireless access networks. A key operational aspect in HetNets is how to optimally implement the wireless backhaul links to efficiently support the traffic demand. In this work, we utilize the recently proposed Robotic Aerial Small Cells (RASCs) that are able to grasp at different tall urban landforms as wireless relay nodes for backhauling. This can be considered as an alternative to fixed small cells (FSCs) which lack flexibility since once installed their position cannot be altered. More specifically, on-demand deployment of RASCs is considered for constructing a millimeter-wave (mmWave) backhaul network to optimize available network capacity using a network flow-based mixed integer linear programming (MILP) formulation. Numerical investigations reveal that for the same required achievable throughput, the number of RASCs required are 25\% to 65\% less than the number of required FSCs. This result can have significant implications in reducing required wireless network equipment (capex) to provide a given network capacity and allows for an efficient and flexible network densification.

cs.NI↗

arXiv2022

The Energy-Delay Pareto Front in Cache-enabled Integrated Access and Backhaul mmWave HetNets

In this paper, to address backhaul capacity bottleneck and concurrently optimize energy consumption and delay, we formulate a novel weighted-sum multi-objective optimization problem where popular content caching placement and integrated access and backhaul (IAB) millimeter (mmWave) bandwidth partitioning are optimized jointly to provide Pareto efficient optimal non-dominating solutions. In such integrated networks analysis of what-if scenarios to understand trade-offs in decision space, without losing sight of optimality, is important. A wide set of numerical investigations reveal that compared with the nominal single objective optimization schemes such as optimizing only the delay or the energy consumption the proposed optimization framework allows for a reduction of the aggregation of energy consumption and delay by an average of 30% to 55%.

math.OC↗

arXiv2022

Energy-Efficient Proactive Caching with Multipath Routing

The ever-continuing explosive growth of on-demand content distribution has imposed great pressure on mobile/wireless network infrastructures. To ease congestion in the network and to increase perceived user experience, caching of popular content closer to the end-users can play a significant role and as such this issue has received significant attention over the last few years. Additionally, energy efficiency is treated as a fundamental requirement in the design of next-generation mobile networks. However, there has been little attention to the overlapping area between energy efficiency and network caching especially when considering multipath routing. To this end, this paper proposes an energy-efficient caching with multipath routing support. The proposed scheme provides a joint anchoring of popular content into a set of potential caching nodes with optimized multipath support whilst ensuring a balance between transmission and caching energy cost. The proposed model also considers different content delivery modes, such as multicast and unicast. Two separated Integer-Linear Programming (ILP) models are formulated for each delivery mode. To tackle the curse of dimensionality we then provide a greedy simulated annealing algorithm, which not only reduces the time complexity but also provides a competitive performance. A wide set of numerical investigations reveal that the proposed scheme reduces the energy consumption up to 80% compared with other widely used caching approaches under the premise of network resource limitation. Sensitivity analysis to different parameters is also meticulously discussed in this paper.

cs.NI↗

arXiv2022

Learning from Images: Proactive Caching with Parallel Convolutional Neural Networks

With the continuous trend of data explosion, delivering packets from data servers to end users causes increased stress on both the fronthaul and backhaul traffic of mobile networks. To mitigate this problem, caching popular content closer to the end-users has emerged as an effective method for reducing network congestion and improving user experience. To find the optimal locations for content caching, many conventional approaches construct various mixed integer linear programming (MILP) models. However, such methods may fail to support online decision making due to the inherent curse of dimensionality. In this paper, a novel framework for proactive caching is proposed. This framework merges model-based optimization with data-driven techniques by transforming an optimization problem into a grayscale image. For parallel training and simple design purposes, the proposed MILP model is first decomposed into a number of sub-problems and, then, convolutional neural networks (CNNs) are trained to predict content caching locations of these sub-problems. Furthermore, since the MILP model decomposition neglects the internal effects among sub-problems, the CNNs' outputs have the risk to be infeasible solutions. Therefore, two algorithms are provided: the first uses predictions from CNNs as an extra constraint to reduce the number of decision variables; the second employs CNNs' outputs to accelerate local search. Numerical results show that the proposed scheme can reduce 71.6% computation time with only 0.8% additional performance cost compared to the MILP solution, which provides high quality decision making in real-time.

cs.NI↗

arXiv2021

A Minmax Utilization Algorithm for Network Traffic Scheduling of Industrial Robots

Emerging 5G and beyond wireless industrial virtualized networks are expected to support a significant number of robotic manipulators. Depending on the processes involved, these industrial robots might result in significant volume of multi-modal traffic that will need to traverse the network all the way to the (public/private) edge cloud, where advanced processing, control and service orchestration will be taking place. In this paper, we perform the traffic engineering by capitalizing on the underlying pseudo-deterministic nature of the repetitive processes of robotic manipulators in an industrial environment and propose an integer linear programming (ILP) model to minimize the maximum aggregate traffic in the network. The task sequence and time gap requirements are also considered in the proposed model. To tackle the curse of dimensionality in ILP, we provide a random search algorithm with quadratic time complexity. Numerical investigations reveal that the proposed scheme can reduce the peak data rate up to 53.4% compared with the nominal case where robotic manipulators operate in an uncoordinated fashion, resulting in significant improvement in the utilization of the underlying network resources.

cs.RO↗

arXiv2021

Airborne Urban Microcells with Grasping End Effectors: A Game Changer for 6G Networks?

Airborne (or flying) base stations (ABSs) embedded on drones or unmanned aerial vehicles (UAVs) can be deemed as a central element of envisioned 6G cellular networks where significant cell densification with mmWave/Terahertz communications will be part of the ecosystem. Nonetheless, one of the key challenges facing the deployment of ABSs is the inherent limited available energy of the drone, which limits the hovering time for serving ground users to the orders of minutes. This impediment deteriorate the performance of the UAV-enabled cellular network and hinders wide adoption and use of the technology. In this paper, we propose robotic airborne base stations (RABSs) with grasping capabilities to increase the serving time of ground users by multiple orders of magnitude compared to nominal hovering based operation. More specifically, to perform the grasping task, the RABS is equipped with a versatile, albeit, general purpose gripper manipulator. Depending on the type of the gripper RABS can provide service in the range of hours, compared to minutes of hovering based ABSs. In theory it is possible that grasping can be energy neutral, hence the time of service can be bounded by the communications energy consumption. To illustrate the case, energy consumption comparison between hovering and grasping is performed in order to reveal the significant benefits of the proposed approach. Finally, overarching challenges, design considerations for RABS, and future avenues of research are outlined to realize the full potential of the proposed robotic aerial base stations.

cs.NI↗